CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables

• URL: http://arxiv.org/abs/2403.01673v1
• Date: Mon, 4 Mar 2024 01:52:40 GMT
• Title: CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
• Authors: Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
• Abstract summary: We introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism. Even with a basic 2-layer as core predictor, CATS achieves state-of-the-art, significantly reducing complexity and parameters compared to previous multivariate models.
• Score: 9.95711569148527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones. To address the difficiency in multivariate models, we introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism, which generates Auxiliary Time Series (ATS) from Original Time Series (OTS) to effectively represent and incorporate inter-series relationships for forecasting. Key principles of ATS - continuity, sparsity, and variability - are identified and implemented through different modules. Even with a basic 2-layer MLP as core predictor, CATS achieves state-of-the-art, significantly reducing complexity and parameters compared to previous multivariate models, marking it an efficient and transferable MTSF solution.

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